Motor current spectrum analysis (MCSA) is a cost effective and non-intrusive method for the condition monitoring of rotating equipment. The operational conditions of rotating equipment can be analyzed and related to the maintenance needs of such equipment. In analyses of this type, mechanical vibrations in alternating current (AC) rotating equipment are transduced back to the power lines via the motor. Electrical motor faults are also transduced back to the power lines. These transduced signals are in actuality very small amplitude modulators to the large AC power line current. As a result, the motor current signal can be characterized as a large sinusoidal carrier wave with small amplitude modulations (see 10 in FIG. 1), where the carrier wave is the result of the power source and the small amplitude modulations are the result of the motor and the mechanical system attached to the motor. In addition, the carrier wave is slightly phase and/or frequency modulated due to its nonideal power source. Consequently, the carrier wave is not time stable.
In the utilization of these signals, a current transformer is attached to a lead of an electrical motor, then these signals are conditioned, sampled and analyzed in the frequency-domain with the Discrete Fourier Transform (DFT). Vibration and motor fault data are displayed as peaks in the spectra. However, the large power line signal and its harmonics are also displayed in the spectra at a magnitude that can be several orders of magnitude greater than the signals of interest. The spectral content due to the large, unstable power line signal and its harmonics is expansive and Gaussian in nature when the motor current is sampled by conventional methods. As a result, any anomalies or abnormalities that have a frequency close to the frequency of the carrier wave and its harmonics are difficult to evaluate and precisely define (see FIG. 16).
The expansive spectral distribution, or spread, of the carrier wave and its harmonics over a range of frequencies in the DFT can be caused by several factors. Two of the more dominant factors are discussed below in detail. First, a nonstationary sinusoidal signal contains a range of frequencies, yielding a spectra with distributed energy about its average frequency. Since the carrier wave in the motor current can be characterized as a nonstationary sinusoidal signal, its resulting spectra will likewise contain a distribution of energy about its average frequency. Second, the process through which real, continuous, time-domain signals are transformed to the discrete frequency-domain can influence frequency resolution in the discrete spectra. The frequency-domain transformation of continuous time-domain signals utilizes the continuous Fourier transform and assumes the signal is infinite in duration. The DFT, however, operates on finite duration discrete time-domain signals. Consequently, the discrete time-domain signal may contain discontinuities at its beginning and ending data points, yielding illegitimate frequency content in the discrete spectra. To overcome this problem, windowing techniques have been developed to smooth these discontinuities prior to performing the DFT and, consequently, minimize their spectral content. However, as a side effect of windowing, the spectral content of authentic components is distributed over a range of frequencies. Since the motor current spectral components due to the carrier wave and its harmonics are very large in relation to the modulators, they will most drastically be influenced by windowing. Therefore, the frequency distribution of the carrier wave and its harmonics in the motor current spectra will be expansive due to windowing.
By employing conventional discrete data techniques, such as windowing, to motor current signals, determining the "health" of the rotating machine with the resulting spectra would be challenging because of the frequency content of spectral components due to the large, nonstationary carrier wave and its harmonics. In order to fully access the information contained in the motor current spectra concerning the health of the rotating machine, the expansive frequency distribution of spectral components due to the carrier wave and its harmonics must be addressed and resolved.
In certain prior art, the small modulators are demodulated from the carrier wave prior to sampling the motor current signal. While the signal to noise ratio is improved, since the carrier is eliminated prior to sampling the motor current, the bandwidth of the resulting spectrum is limited. As a consequence, higher frequency modulators cannot be analyzed since they are outside of the permissible frequency range. By eliminating the possibility of analyzing these higher frequency motor current components, only a limited knowledge can be gained concerning the health of the rotating machine.
In other certain prior art, methods have been implemented for initiating the sampling process with a real-time event. These methods determine when to begin sampling at a fixed rate and are useful in analyzing transients in the discrete time-domain. However, frequency-domain analysis is not improved since the frequency distribution of spectral components due to the carrier wave and its harmonics will be expansive due to both the effects of windowing and the fact that the carrier is nonstationary.
Accordingly, it is an object of the present invention to provide a method and apparatus to detect small amplitude modulators in a carrier wave at all frequencies with improved spectral frequency resolution by minimizing the distribution of those spectral components due to the carrier wave and its harmonics.
It is a further object of the invention to provide spectral frequency resolution enhancement by collecting a sampled data set completely filled with an exact whole number of stationary carrier wave cycles. The exact whole number of carrier wave cycles is accomplished with the invention method by insuring that the product of the carrier wave frequency and the time duration of the sampled data set equals a whole number. This whole number is the number of carrier wave cycles in the sampled data set.
Within this invention, the sampled data set duration is defined as the number of samples in the data set divided by the sampling rate. Therefore, a sampled data set completely filled with a whole number of carrier wave cycles can be created by selecting appropriate values for both the number of samples in the data set and the sampling rate.
It is also an object of the present invention to provide a method that specifies the actual sampling rate to be equal to the number of samples in the discrete data set multiplied by the carrier wave frequency divided by the number of whole carrier wave cycles in the sampled data set. The actual sampling rate should be selected so that it conforms to this relationship and is close in frequency to the desired sampling rate, which must be at least twice the desired maximum frequency to be processed by the DFT.
It is another object to create stationary discrete time-domain carrier waves from nonstationary analog time-domain carrier waves with apparatus of the present invention by varying the sampling rate with the frequency variations of the analog carrier wave. This is defined as "dynamically adjusting the sampling rate". Creating a stationary sampled signal from the nonstationary analog motor current signal is not possible by sampling the motor current signal at a fixed rate, as is traditionally done. As a result, the sampling rate must change with the motor current signal frequency variations.
Another object of the present invention is to provide for changing the sampling rate by a variable frequency clock generator, whose output frequency adjusts with the carrier wave frequency.
A further object is to provide for the implementation of the adjustable frequency clock generator which adjusts to and tracks with the variable frequency carrier wave through the use of a phase locked loop (PLL) circuit. The use of the PLL as a practical means of implementing the sampling process imposes the condition that 2.sup.n samples are collected for each carrier wave cycle and the sampled data set consists of 2.sup.n complete carrier wave cycles. In addition, to maximize its ability to accurately track the variation in frequency of the carrier wave, the adjustable frequency clock generator is specially configured. By doubling the frequency of the carrier wave prior to input into the PLL, the comparitor section of the PLL is forced to track twice as often for every sampled data set. As a result, the time variation between samples in the discrete data set is minimized and the analog signal is more faithfully replicated in the discrete time-domain.
Still another object of the present invention is to provide a method for enhancing motor system fault detection wherein no signal conditioning beyond that required by the Niquist criteria is performed. Rather, the process of converting a time-domain analog signal to the discrete frequency-domain is modified and the sampling rate is conditioned so that it adjusts to the frequency variations of the nonstationary analog carrier wave. Nor is windowing of the discrete time-domain data required as a part of this process since the sampled data set contains no discontinuities. Finally, since the motor current is conditioned only with a low pass filter, as required by the Niquist criteria, all information contained in the motor current spectra remains, providing the opportunity to more thoroughly determine the health of the entire rotating machine. By acquiring motor current data in this fashion, the spectral distribution of the carrier wave frequency and all its harmonics will be minimized, creating a motor current spectra with enhanced frequency resolution.
Other objects and advantages over the prior art will become apparent to those skilled in the art upon reading the detailed description together with the drawings as described as follows.